U.S. patent application number 09/854200 was filed with the patent office on 2002-01-31 for composite image generating method, and a fingerprint detection apparatus.
Invention is credited to Benckert, Henrik.
Application Number | 20020012455 09/854200 |
Document ID | / |
Family ID | 20279649 |
Filed Date | 2002-01-31 |
United States Patent
Application |
20020012455 |
Kind Code |
A1 |
Benckert, Henrik |
January 31, 2002 |
Composite image generating method, and a fingerprint detection
apparatus
Abstract
A fingerprint detection apparatus has a fingerprint sensor (10),
which produces a sequence of at least partially overlapping
fingerprint frames (13), when a finger (11) is moved in relation to
the fingerprint sensor. The apparatus also has a processing device,
which is coupled to the fingerprint sensor and produces a complete
fingerprint image (14) by computing a relative displacement between
respective fingerprint frames and combining the fingerprint frames
accordingly. The processing device determines the relative
displacement between a first fingerprint frame and a second
fingerprint frame by selecting a plurality of subareas in the first
fingerprint frame. For each subarea, a respective correlation with
the second fingerprint frame is determined. Then a linear
combination of the respective correlations is computed, and
finally, from the computed linear combination, the relative
displacement is determined.
Inventors: |
Benckert, Henrik; (Lund,
SE) |
Correspondence
Address: |
Ronald L. Grudziecki
BURNS, DOANE, SWECKER & MATHIS, L.L.P.
P.O. Box 1404
Alexandria
VA
22313-1404
US
|
Family ID: |
20279649 |
Appl. No.: |
09/854200 |
Filed: |
May 11, 2001 |
Current U.S.
Class: |
382/124 ;
382/278; 382/282; 382/284 |
Current CPC
Class: |
G06V 40/1335
20220101 |
Class at
Publication: |
382/124 ;
382/282; 382/278; 382/284 |
International
Class: |
G06K 009/00; G06K
009/36; G06K 009/20; G06K 009/64 |
Foreign Application Data
Date |
Code |
Application Number |
May 15, 2000 |
SE |
0001761-6 |
Claims
1. A method of generating a composite image (14) from a sequence of
partial image frames (13a-x), which represent different but at
least partially overlapping areas of a body-specific pattern,
wherein displacements between successive image frames are
determined so as to combine the image frames correctly when
generating the composite image, characterized in that the following
steps are performed for said sequence of image frames: selecting a
predetermined number of subareas (22a-f) of a first image frame
(13j), for each subarea, determining a correlation (25) with a
second image frame (13), which succeeds the first image frame,
calculating a linear combination (29) of the correlations (25a,
25b) for all subareas, and determining, from the calculated linear
combination, the displacement between the first and second image
frames.
2. A method as in claim 1, wherein portions of said first image
frame (13j), which are uncommon compared to other portions of the
image frame and which moreover are non-adjacent to each other in
the image frame, are selected as said subareas (22a-f).
3. A method as in claim 1 or 2, wherein the image frames (13a-x)
are grayscale images and wherein the selected subareas (22a-f) have
pixel grayscale intensities, which are either essentially white or
essentially black.
4. A method as in any of claims 1-3, wherein each subarea contains
one pixel only.
5. A method as in claim 4, wherein a total of six subareas (22a-f)
are selected, three of them being essentially black and the other
three being essentially white.
6. A method as in any preceding claim, wherein the correlation for
each subarea (22) of the first image frame (13j) is determined with
respect to a search area (23) of the second image frame (13), said
search area being larger than said subarea but being smaller than
the second image frame as a whole.
7. A method as in claim 6, wherein the search area (23) of the
second image frame (13) comprises 19.times.19 pixels.
8. A method as in claim 1, wherein the displacement is determined
by first selecting, from the calculated combination, a plurality of
displacement candidates and then applying a Maximum-Likelihood
estimating procedure to the plurality of displacement candidates,
so as to find the most likely displacement.
9. A method as in any preceding claim, wherein said composite image
(14) and said sequence of partial image frames (13a-13x) represent
a fingerprint.
10. A fingerprint detection apparatus, comprising a fingerprint
sensor (10) adapted to produce a sequence of at least partially
overlapping fingerprint frames (13), when a finger (11) is moved in
relation to the fingerprint sensor, and a processing device (15),
which is coupled to the fingerprint sensor and is adapted to
produce a complete fingerprint image (14) by computing the relative
displacement between respective fingerprint frames and combining
the fingerprint frames accordingly, characterized in that the
processing device (15) is adapted to determine the relative
displacement between a first fingerprint frame (13j) and a second
fingerprint frame (13) by: selecting a plurality of subareas
(22a-f) in the first fingerprint frame; for each subarea,
determining a respective correlation (25) with the second
fingerprint frame; computing a linear combination (2a) of the
respective correlations (25a, 25b), and determining, from the
computed linear combination, the relative displacement.
11. A fingerprint detection apparatus as in claim 10, wherein the
processing device (15) furthermore is adapted to select, from the
computed linear combination (29), a plurality of displacement
candidates and then determine the most likely displacement among
these candidates by applying a Maximum-Likelihood estimating
procedure.
12. A portable or miniaturized electronic device, comprising a
fingerprint detection apparatus according to any of claim 10 or
11.
13. A device according to claim 12 in the form of a mobile
telephone (1).
Description
TECHNICAL FIELD
[0001] Generally speaking, the present invention relates to the
field of biometrics, i.e. identification of an individual based on
his/her physiological or behavioral characteristics. More
specifically, the present invention relates to a method of
generating a composite image from a sequence of partial image
frames, which represent different but at least partially
overlapping areas of a body-specific pattern, such as a
fingerprint.
[0002] The invention also relates to a fingerprint detection
apparatus of the type having a fingerprint sensor, which is adapted
to produce a sequence of at least partially overlapping fingerprint
frames, when a finger is moved in relation to the fingerprint
sensor, and furthermore having a processing device, which is
coupled to the fingerprint sensor and is adapted to produce a
complete fingerprint image by determining relative displacements
between respective fingerprint frames and combining the fingerprint
frames accordingly.
PRIOR ART
[0003] Fingerprint-based biometrics systems are used in various
applications for identifying an individual user or verifying
his/her authority to perform a given act, to access a restricted
area, etc. Fingerprint identification is a reliable biometrics
technique, since no two fingerprints from different individuals
have the same body-specific pattern of ridges and valleys.
Furthermore, the fingerprint pattern of an individual remains
unchanged throughout life.
[0004] Some fingerprint detection systems operate by capturing a
complete image of a fingerprint in one step; the surface of the
finger is recorded by e.g. capturing a grayscale photographic
picture, which subsequently may be analyzed through image
processing methods in order to determine whether or not the
captured fingerprint corresponds to prestored reference data.
[0005] Other fingerprint detection systems do not produce the
entire fingerprint image in just one step. Instead, the surface of
the finger is scanned, producing a sequence of fingerprint frames
or "slices", which are combined into a composite image representing
the entire fingerprint. EP-A2-0 929 050 discloses a scanning
capacitive semiconductor fingerprint detector, which includes an
array of capacitive sensing elements. When a user moves his/her
finger across the scanning array, a sequence of partial fingerprint
frames are produced by the capacitive sensing elements. The partial
fingerprint frames are assembled into a composite fingerprint
image.
[0006] EP-A1-0 813 164 relates to a reading system for digital
fingerprints, comprising a sensor in the form of a bar, which is
wider than the typical width of a finger but is relatively narrow
compared to the length of the finger. The sensor is an integrated
circuit with an active layer, which is sensitive to pressure and/or
temperature. When a user moves his/her finger across the sensor,
the sensor will scan the fingerprint and deliver a sequence of
fingerprint frames, each having a size essentially corresponding to
the sensitive area of the sensor and being partially overlapping. A
processing unit receives the sequence of fingerprint frames from
the sensor and is adapted to reconstruct a complete fingerprint
image. Once the complete fingerprint image has been obtained, it
may be compared with a reference image, which is stored on e.g. a
smart card, in order to authenticate the holder of the smart
card.
[0007] Fingerprint sensors of the above type, which provide a
sequence of partially overlapping fingerprint frames, have several
advantages, particularly in the field of miniaturized or portable
electronic devices. A moderate cost and a low power consumption,
together with a small mounting area requirement, are important
advantages in this respect. However, miniaturized or portable
electronic devices often have a limited data processing capacity;
both the data processor (CPU) and the electronic memories used
therein are adapted to portable use and consequently do not have as
excellent performance as for instance some stationary
installations.
[0008] To combine a sequence of partially overlapping fingerprint
frames into a composite image representing the whole of a
fingerprint is a computationally intensive operation. In order to
assemble the composite fingerprint image from a number of
successive fingerprint frames, a displacement vector between each
pair of frames must be calculated. The standard method is to use
autocorrelation, wherein a first fingerprint frame and a succeeding
second frame are read and an output image or correlation map is
produced in displacement coordinate space. The correlation map
normally contains a global maximum at a point corresponding to the
displacement vector.
[0009] More specifically, in the case of digital grayscale
fingerprint frames, two two-dimensional arrays of integers numbers
are supplied to the autocorrelation procedure, which outputs one
two-dimensional array of integers, where these integer numbers can
be illustrated as grayscale intensities. As already mentioned,
autocorrelation is computationally intensive and is hard to perform
in real time with a data processor, which is optimized for low
power consumption and miniaturized or portable applications.
Furthermore, the computational effort grows quadratically with the
size of the computational area.
SUMMARY OF THE INVENTION
[0010] In view of the above problems, an object of the invention is
to facilitate the production of a composite image, representing a
body-specific pattern such as a fingerprint, from a sequence of at
least partially overlapping image frames in real time by using much
less power than conventional methods, thereby allowing small
sensors (which are not capable of producing a complete image in one
step) to be used in miniaturized or portable applications.
[0011] This object is achieved by a method and an apparatus
according to the attached independent patent claims. More
specifically, a composite image may be produced from a sequence of
at least partially overlapping image frames by performing the
following steps for the sequence of image frames. A predetermined
number of subareas (which can be as small as 1.times.1 pixel) are
selected in a first image frame. For each of these subareas, a
correlation with a second image frame, succeeding the first image
frame, is determined. A linear combination of the correlations for
all subareas are then calculated, and finally, from the calculated
linear combination, the displacement between the first and second
image frames is determined. Once the displacement is known, the
first and second image frames may be correctly assembled. By
repeating the above procedure, a composite fingerprint image, etc,
may be stepwise assembled.
[0012] The object is also achieved by a fingerprint detection
apparatus, which has a fingerprint sensor adapted to produce a
sequence of at least partially overlapping fingerprint frames, and
a processing device, which is adapted to perform the above
method.
[0013] Other objects, features and advantages of the present
invention will appear from the following detailed disclosure, from
the attached subclaims as well as from the drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] A preferred embodiment of the present invention will now be
described in more detail, reference being made to the enclosed
drawings, in which:
[0015] FIG. 1 is a schematic view of a portable electronic device,
in the form of a mobile telephone, in which the fingerprint
detection method and apparatus according to the invention may be
used,
[0016] FIG. 2 illustrates the overall operation principle of a
scanning-type fingerprint sensor, which produces a sequence of
partially overlapping fingerprint frames,
[0017] FIG. 3 is a block diagram, which illustrates the essential
components of the mobile telephone illustrated in FIG. 1,
[0018] FIG. 4 is a flowchart diagram, which illustrates the steps
of the method according to the preferred embodiment,
[0019] FIG. 5 illustrates a sequence of partially overlapping
fingerprint frames and the operations made during a preprocessing
phase of the method according to the preferred embodiment,
[0020] FIG. 6 is an enlarged view of a part of a fingerprint frame
and illustrates the operations made for that frame during an
autocorrelation phase of the method,
[0021] FIG. 7 illustrates a correlation map as a result of
aforesaid autocorrelation, and
[0022] FIG. 8 illustrates how individual correlation maps are put
together in a linear combination so as to generate a total
correlation map, from which the displacement vector is
determined.
DETAILED DISCLOSURE
[0023] In FIG. 1 there is shown a portable electronic device in a
form of a mobile telephone 1. The mobile telephone 1 will be used
as an example, in a non-limiting sense, of one possible portable
application, in which the method and apparatus of the invention may
be used. The mobile telephone 1 comprises a first antenna 2, which
is adapted to establish and maintain a first radiolink 2' to a base
station 8 in a mobile telecommunications system, such as GSM
("Global System for Mobile communications"). The telephone 1 also
has a second antenna 3, which is used for communicating with a
remote device 9 over a second radio link 3'. The second antenna 3
may for instance be adapted for Bluetooth or another kind of
short-range supplementary data communication, e.g. on the 2.4 GHz
ISM band ("Industrial, Scientific and Medical").
[0024] As any other contemporary mobile telephone, the telephone 1
comprises a loudspeaker 4, a display 5, a set of cursor keys 6a, a
set of alphanumeric keys 6b and a microphone 7. In addition to
this, the telephone 1 also comprises a fingerprint sensor 10, which
forms a part of the fingerprint detection apparatus according to
the invention and is used by the method according to the
invention.
[0025] According to the preferred embodiment, the fingerprint
sensor 10 is a thermal silicon chip fingerprint sensor called
FingerChip.TM. which is commercially available from Thomson-CSF
Semiconducteur Specifiques, Route Departementale 128, BP 46, 91 401
Orsay Cedex, France. The FingerChip.TM. fingerprint sensor uses the
heat generated by the finger in order to produce an eight-bit, 500
dpi grayscale image of a fingerprint. The imaging surface of the
FingerChip.TM. sensor measures 1.5 mm.times.14 mm. When a finger 11
is moved in a direction 12 across the fingerprint sensor 10 (see
FIG. 2), the sensor 10 produces a sequence of partially overlapping
fingerprint frames 13a, 13b, . . . , 13x every 20 ms, each frame
representing an area approximately equal to the imaging surface
mentioned above. The frames are supplied to a data
processor/controller 15 of the mobile telephone 1 (see FIG. 3). The
data processor/controller 15 will then produce a composite
fingerprint image 14, as will be described in more detail
later.
[0026] Alternatively, virtually any other commercially available
fingerprint sensor may be used, such as an optical sensor, a
capacitive sensor, etc, as long as such a sensor is capable of
producing a sequence of partially overlapping fingerprint frames,
as described above.
[0027] As shown in FIG. 3, the mobile telephone 1 comprises the
data processor/controller 15, which as already mentioned is
connected to the fingerprint sensor 10. The data
processor/controller 15 is also coupled to a primary memory 16,
such as any commercially available RAM memory. It is also connected
to a permanent memory 17, which may be implemented by any
commercially available non-volatile electronic, magnetic,
magnetooptical, etc memory.
[0028] The permanent memory 17 comprises program code 18, that
defines a set of program instructions, which when executed by the
dataprocessor/controller 15 perform the method according to the
invention as well as many other tasks within the mobile telephone
1. The permanent memory 17 also comprises fingerprint reference
data 19, which are used by the controller 15, after having
assembled a composite fingerprint image, for comparing this image
to the fingerprint reference data so as to determine whether or not
the owner of the finger 11, i.e. the user of the mobile telephone
1, has the identity represented by the fingerprint reference data
19.
[0029] In addition to the above, the dataprocessor/controller 15 is
coupled to GSM radio circuitry 20 for implementing the first
radiolink 2' through the antenna 2. The dataprocessor/controller 15
is also connected to a Bluetooth radio 21, which in turn is coupled
to the Bluetooth antenna 3. Finally, the dataprocessor/controller
15 is connected to the display 5.
[0030] All of the components described above, including the
dataprocessor/controller 15, may be implemented in many different
ways by any commercially available components, which fulfill the
functional demands described below. As regards the
dataprocessor/controller 15, is may be implemented by any
commercially available microprocessor, CPU, DSP, or any other
programmable electronic logic device.
[0031] Referring now to the remaining FIGS. 4-8, the method and the
apparatus according to the preferred embodiment will be described.
Generally speaking, the invention is based on the idea of splitting
up the image area used for the autocorrelation between successive
fingerprint frames into several small subareas. Since, as explained
above, the computation scales quadratically with the image area
involved, the resulting total computation may be drastically
reduced thanks to the invention. Once autocorrelation has been
performed for these smaller subareas, and the respective
correlation maps have been produced, a total autocorrelation map is
calculated as a linear combination of the correlation maps
resulting from the autocorrelations with the individual subareas.
The displacement vector between two successive frames is determined
from the total autocorrelation map, possibly by applying a
Maximum-Likelihood procedure to the total correlation map, if there
appears to be more than one displacement vector candidate.
[0032] Since the autocorrelation, which is an important part of the
process of assembling a plurality of partially overlapping
fingerprint frames into a composite fingerprint image, may be made
with far less computational power than conventional
autocorrelation, the invention allows a small fingerprint sensor to
be used in a miniaturized or portable application having a very low
power consumption as well as limited computational resources.
[0033] Referring now to FIG. 4, a fingerprint autocorrelation
routine 100 is illustrated, which is an important part of the
method according to the invention. After necessary initialization
etc, a first phase or pre-processing phase 110 is entered. In a
step 112, a first fingerprint frame is received in the
dataprocessor/controller 15 from the fingerprint sensor 10. In a
subsequent step 114, the grayscale pixel intensity of the first
fingerprint frame is analyzed. A plurality of small and
geographically distributed subareas #l-#n are then selected, in a
step 116, in the first fingerprint frame, as is illustrated in more
detail in FIG. 5. The selected subareas are the ones that are
statistically uncommon with respect to the image frame as a whole.
The reason for this is that it will be easier to autocorrelate the
selected subareas with the successive fingerprint frame.
[0034] FIG. 5 illustrates, in the left hand column, a plurality of
successive fingerprint frames 13a, . . . 13j, as received from the
fingerprint sensor 10. Moreover, in the right hand column, the
frames 13a-13j are again illustrated (as 13a'-13j'), where the
selected uncommon subareas are indicated as the respective centers
of small white squares. For each fingerprint frame a total of six
uncommon subareas are selected in the preferred embodiment. For the
last frame 13j/13j' a magnification is provided at the bottom of
FIG. 5. From this magnification the selected uncommon subareas 22a,
22b, 22c, 22d, 22e and 22f appear more clearly.
[0035] According to the preferred embodiment, each selected subarea
has a size of 1.times.1 pixel, i.e. a single pixel having a
grayscale value from e.g. 0-255. Normally, in a fingerprint frame
representing a part of a fingerprint, uncommon areas are the ones
that either have a very high intensity (i.e. are approximately
white) or have a very low intensity (i.e. are approximately black).
Therefore, according to the preferred embodiment, the selected
subareas are pixels, which preferably are either white (or almost
white) or black (or almost black). According to the preferred
embodiment, three white pixels and three black pixels are selected
as subareas #1-#6 in the fingerprint frame. However, in certain
situations (such as image saturation) it may be more appropriate to
select other than white or black pixels as uncommon areas.
Alternatively, larger uncommon subareas may be selected in the
fingerprint frame, such as 4.times.4 pixels. In such a case, an
uncommon area may be an area, which contains an intensity gradient
(changing from black to white, or vice versa). Generally speaking,
by using a larger size of each subarea, a smaller number of
subareas may be used, and vice versa.
[0036] Once the uncommon subareas have been selected in step 116,
the execution continues to a second phase or autocorrelation phase
120. The purpose of the autocorrelation is to locate the respective
subareas selected during the pre-processing of one fingerprint
frame in a successive fingerprint frame. Since statistically
uncommon pixels are used as subareas, it is likely that just one
match will be found in a search area in the successive fingerprint
frame. The combined autocorrelation results in at least one
displacement vector candidate. A displacement vector represents the
relative displacement between a given fingerprint frame and its
successor. Once the displacement vector has been determined, the
two fingerprint frames may be combined so as to successively
assemble the composite fingerprint image.
[0037] From a general point of view, autocorrelation coefficients
c.sub.i,j represent the degree of correlation between neighboring
data observations (successive fingerprint frames) in a time series.
One autocorrelation, which may be used within the scope of the
invention, is
[0038] C.sub.ij=.sigma..sub.x,y(f(x,y)-g(x+i,y+j)).sup.2
[0039] where f(x,y) is a selected subarea in a given fingerprint
image and g(x,y) is a subarea in the successive fingerprint frame.
The object of the autocorrelation, which is to find the largest
correlation between two fingerprint frames, is obtained by
calculating c.sub.i,j for a search area 23 (FIG. 6) in the
successive fingerprint frame 13. The best match is found by
minimizing c.sub.i,j over the search area 23. According to the
preferred embodiment, the search area is a 19.times.19 pixel area.
However, other search areas, even nonsquare ones, are equally
possible.
[0040] The use of the above autocorrelation is illustrated in FIG.
6, where a portion of a successive fingerprint frame 13 is shown.
The small square 22 represents one of the selected subareas during
the pre-processing. The size of the subarea 22 as well as the
search area 23 have been enlarged in FIG. 6 for reasons of clarity.
The center 24 of the search area 23 of the successive finger print
frame represents the position in the preceding fingerprint frame,
where the subarea 22 was known to be located. By minimizing
c.sub.i,j over the search area 23 in the successive fingerprint
frame as described above, the location of the subarea 22 in the
successive fingerprint frame 13 may be found.
[0041] The result of the autocorrelation for an individual subarea
22 with respect to a search area 23 is a 19.times.19 pixel
correlation map 25, which is illustrated in FIG. 7. The minimum of
the autocorrelation c.sub.i,j appears as a dark pixel 28 in the
correlation map 25. A displacement vector 26 may then easily be
determined, having its starting point in the center 27 of the
correlation map 25 and its ending point at the minimum intensity
pixel (black pixel) 28.
[0042] The above corresponds to step 122 in the flowchart of FIG.
4. As already mentioned, in the preferred embodiment, a total of
six correlation maps are produced for a total of six subareas 22.
In a subsequent step 124, a total correlation map 29 is computed as
a linear combination (sum) of the individual correlation maps 25a,
25b, etc, as shown in FIG. 8. From the total correlation map 29 it
is possible to determine one displacement vector, or often a number
of displacement vector candidates.
[0043] The reason why, according to the invention, the
autocorrelation may be performed for several very small and
geographically distributed subareas 22 of successive fingerprint
frames 13, which are finally combined into a total correlation map
29, is that a fingerprint image has a characteristic pattern with
lines (ridges and valleys) in different directions. These lines
will also appear in the correlation maps. If a subarea is shifted
along the fingerprint lines, the correlation will be very strong
and lines will appear. This is also true, if the subarea is shifted
by the width of one line. When the autocorrelation is calculated
for parts of the fingerprint, where the lines have different
angles, the correlation maps 25 will also exhibit lines in
different directions. When a sufficient number of maps 25a, 25b, .
. . are combined, the lines will cross throughout the total
correlation map 29, but there will be a clear superposition at the
correlation minimum, from which the displacement vector may be
determined.
[0044] Often, however, several minimum-intensity pixels 28 may
appear in a total correlation map 29, thereby indicating not a
single displacement vector but a number of displacement vector
candidates. These are determined in a subsequent step 126 of the
flowchart shown in FIG. 4 and are further processed in a
post-processing phase 130 of the fingerprint autocorrelation
routine 100.
[0045] During the post-processing of the total correlation map 29
(or more specifically the number of displacement vector candidates
determined in step 126), a Maximum-Likelihood estimating procedure
is applied to these displacement candidates, the assumption being
that it is probable that the correct minimum for a certain
correlation map 29 (as determined for a certain fingerprint frame
13) is close to the minimum in the corresponding correlation map of
the preceding fingerprint frame. Consequently, the probability that
the minimum has moved from one position to another is proportional
to the distance between the minima. If this probability is
maximized over time, the most likely displacement path is
found.
[0046] In more detail, we define a displacement vector as the
direction and size of a displacement between two successive
fingerprint frames. As already mentioned, the displacement vector
appears in a correlation map as a minimum-intensity pixel (black
pixel). Moreover, we define a transition probability as the
probability of the vector moving from a first position to a second
position.
[0047] The displacement vector is a function of finger movement and
represents finger speed and direction. A vector transition
corresponds to an acceleration. Since the finger is a physical
body, it cannot have an infinitely large acceleration. Therefore,
if the finger movement is sampled at a sufficient rate, the samples
will be highly correlated. Because of this correlation, we can
calculate the probabilities of the vector moving from one position
to another. Therefore, for all positions, we define a corresponding
probability of the displacement vector moving from a first position
to a second position. Statistical data from real user interaction
may be used so as to calculate or "calibrate" the
probabilities.
[0048] Some simplifications can be made in order to reduce the
number of calculations:
[0049] Calculate the probabilities of areas. Map all vectors in an
area to one probability.
[0050] Probabilities are proportional to the distance between first
and second positions. We can assume that small distances between
these two positions are more likely than large distances.
[0051] From the above we may define the following algorithm
(represented by steps 132 and 134 in FIG. 4), which finds the most
likely displacement vector, among the number of displacement vector
candidates determined in step 126, by maximizing the above
probabilities. A search tree is formed of likely vectors, and an
initial breadth search is performed. To reduce the number of
calculations, only the N most likely paths are used. In an
initialization step, we set certain variables equal to 0 for the N
paths. Then a loop is iterated, where we find the N most probable
displacement vectors in a correlation map. These are used as nodes
in the search tree. To rank the displacement vector candidates a
metric is calculated for each candidate. The metric is a function
of transition probability and correlation quality. For each node at
depth t+l, find for each of the predecessors at depth t the sum of
the metric of the predecessor and the branch metric of the
transition. Determine the maximum of these sums and assign it to
this node. Then calculate the sum of the displacement vector and a
total displacement of the selected predecessor, and assign it to
the node. The node with the largest metric is the estimation of the
correct displacement vector.
[0052] The fingerprint detection method and apparatus described
above may be implemented in many different electronic devices,
preferably miniaturized or portable ones. One example is a mobile
telephone 1, as illustrated in FIG. 1. Other examples involve small
hand-held devices to be used as door openers, remote controls,
wireless authenticating devices, devices for performing wireless
electronic payment, etc.
[0053] According to one aspect of the invention, the determined
displacement vector may be held to represent a movement of a user's
finger in a coordinate system. This allows the method and the
apparatus of the invention to be used as a pointing device for
controlling the position of a cursor, etc, on a computer-type
display.
[0054] The invention has been described above with reference to a
preferred embodiment. However, other embodiments than the one
disclosed above are equally possible within the scope of the
invention, as defined by the appended patent claims. In particular,
it is observed that the method according to the invention may be
applied not only to fingerprints but also to other types of images,
which represent a body-specific pattern containing some kind of
periodic character.
* * * * *